Intrusion detection systems play an important role in preventing security threats and protecting networks from attacks. However, with the emergence of unknown attacks and imbalanced samples, ...traditional machine learning methods suffer from lower detection rates and higher false positive rates. We propose a novel intrusion detection model that combines an improved conditional variational AutoEncoder (ICVAE) with a deep neural network (DNN), namely ICVAE-DNN. ICVAE is used to learn and explore potential sparse representations between network data features and classes. The trained ICVAE decoder generates new attack samples according to the specified intrusion categories to balance the training data and increase the diversity of training samples, thereby improving the detection rate of the imbalanced attacks. The trained ICVAE encoder is not only used to automatically reduce data dimension, but also to initialize the weight of DNN hidden layers, so that DNN can easily achieve global optimization through back propagation and fine tuning. The NSL-KDD and UNSW-NB15 datasets are used to evaluate the performance of the ICVAE-DNN. The ICVAE-DNN is superior to the three well-known oversampling methods in data augmentation. Moreover, the ICVAE-DNN outperforms six well-known models in detection performance, and is more effective in detecting minority attacks and unknown attacks. In addition, the ICVAE-DNN also shows better overall accuracy, detection rate and false positive rate than the nine state-of-the-art intrusion detection methods.
In recent years, machine learning-based intrusion detection systems (IDSs) have proven to be effective; especially, deep neural networks improve the detection rates of intrusion detection models. ...However, as models become more and more complex, people can hardly get the explanations behind their decisions. At the same time, most of the works about model interpretation focuses on other fields like computer vision, natural language processing, and biology. This leads to the fact that in practical use, cybersecurity experts can hardly optimize their decisions according to the judgments of the model. To solve these issues, a framework is proposed in this paper to give an explanation for IDSs. This framework uses SHapley Additive exPlanations (SHAP), and combines local and global explanations to improve the interpretation of IDSs. The local explanations give the reasons why the model makes certain decisions on the specific input. The global explanations give the important features extracted from IDSs, present the relationships between the feature values and different types of attacks. At the same time, the interpretations between two different classifiers, one-vs-all classifier and multiclass classifier, are compared. NSL-KDD dataset is used to test the feasibility of the framework. The framework proposed in this paper leads to improve the transparency of any IDS, and helps the cybersecurity staff have a better understanding of IDSs' judgments. Furthermore, the different interpretations between different kinds of classifiers can also help security experts better design the structures of the IDSs. More importantly, this work is unique in the intrusion detection field, presenting the first use of the SHAP method to give explanations for IDSs.
The assembly of the NLRP3 inflammasome can promote the release of IL-1β/IL-18 and initiate pyroptosis. Accordingly, the dysregulation of NLRP3 inflammasome activation is involved in a variety of ...human diseases, including gout, diabetes, and Alzheimer’s disease. NLRP3 can sense a variety of structurally unrelated pathogen-associated molecular patterns (PAMPs) or danger-associated molecular patterns (DAMPs) to trigger inflammation, but the unifying mechanism of NLRP3 activation is still poorly understood. Increasing evidence suggests that intracellular ions, such as K+, Ca2+, and Cl−, have a significant role in NLRP3 inflammasome activation. Here, we review the current knowledge about the role of ionic fluxes in NLRP3 inflammasome activation and discuss how disturbances in intracellular ionic levels orchestrate different signaling events upstream of NLRP3.
Ion fluxes, including K+ efflux, Ca2+ mobilization, and Cl− efflux, have been proposed as crucial events in NLRP3 inflammasome activation.
K+ efflux is a distal upstream event and is sufficient to activate the NLRP3 inflammasome.
The signaling pathways upstream of NLRP3 converge on Ca2+ release from the endoplasmic reticulum, which promotes mitochondrial damage and subsequent NLRP3 inflammasome activation.
Cl− efflux acts downstream of mitochondrial damage and is a proximal upstream event for NLRP3 inflammasome activation.
Intracellular ions can function as signaling messengers, effectively linking distinct events upstream of NLRP3 activation.
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•A novel approach to improving the interfacial compatibility based on fiber coating and subsequent heat treatment was presented.•A ductile fiber/matrix interfacial region with ...relativity lower interfacial shear strength in the SiCf/Ti-6Al-4V composites was obtained.•Interfacial microstructure evolution and reaction dynamics were systematically investigated.•Interfacial microstructures have crucial influence on the interfacial micromechanical properties, interfacial debonding and sliding behaviors.
A new approach to improving the interfacial compatibility of SiCf/Ti-6Al-4V compositesby using fiber coating coupled with subsequent heat treatment was presented. The SiCf/Ti-6Al-4V composites with C-coated and C/Mo-coated fibers were prepared using the foil-fiber-foilmethod and subsequently heat treated in vacuum at 750 °C to obtain several kinds of the SiCf/Ti-6Al-4V composites with different interfacial characteristics. Interfacial microstructures of these composites were systematically characterized by means ofscanning electron microscopy and X-ray energy dispersive spectroscopy to investigate the interfacial microstructure evolution and reaction dynamics. The effects of interfacial microstructures oninterfacial mechanical propertieswere also investigated using thin-slice fiber push-out tests. The results show that the brittle interfacial reaction layer of TiC in theC-coated SiCf/Ti-6Al-4V composites was obviously thickened and some microvoids even formed near the matrix with increasing heat treatment durations, whereas the matrix adjacent to Mo coating in the C/Mo-coated SiCf/Ti-6Al-4V composites gradually transformed into a ductile β-Ti layer. Interfacial shear strengths of the C/Mo-coated composites were slightly increased with increasing the heat treatment time, while those of theC-coated composites were remarkably improved. Critical issues onmodifying interfacial compatibility of the SiCf/Ti composites for further improving the mechanical behavior of the composites were discussed.
To explore the advantages of adversarial learning and deep learning, we propose a novel network intrusion detection model called SAVAER-DNN, which can not only detect known and unknown attacks but ...also improve the detection rate of low-frequent attacks. SAVAER is a supervised variational auto-encoder with regularization, which uses WGAN-GP instead of the vanilla GAN to learn the latent distribution of the original data. SAVAER's decoder is used to synthesize samples of low-frequent and unknown attacks, thereby increasing the diversity of training samples and balancing the training data set. SAVAER's encoder is used to initialize the weights of the hidden layers of the DNN and explore high-level feature representations of the original samples. The benchmark NSL-KDD (KDDTest+), NSL-KDD (KDDTest-21) and UNSW-NB15 datasets are used to evaluate the performance of the proposed model. The experimental results show that the proposed SAVAER-DNN is more suitable for data augmentation than the other three well-known data oversampling methods. Moreover, the proposed SAVAER-DNN outperforms eight well-known classification models in detection performance and is more effective in detecting low-frequent and unknown attacks. Furthermore, compared with other state-of-the-art intrusion detection models reported in the IDS literature, the proposed SAVAER-DNN offers better performance in terms of overall accuracy, detection rate, F1 score, and false positive rate.
The dysregulation of NLRP3 inflammasome can cause uncontrolled inflammation and drive the development of a wide variety of human diseases, but the medications targeting NLRP3 inflammasome are not ...available in clinic. Here, we show that tranilast (TR), an old anti‐allergic clinical drug, is a direct NLRP3 inhibitor. TR inhibits NLRP3 inflammasome activation in macrophages, but has no effects on AIM2 or NLRC4 inflammasome activation. Mechanismly, TR directly binds to the NACHT domain of NLRP3 and suppresses the assembly of NLRP3 inflammasome by blocking NLRP3 oligomerization. In vivo experiments show that TR has remarkable preventive or therapeutic effects on the mouse models of NLRP3 inflammasome‐related human diseases, including gouty arthritis, cryopyrin‐associated autoinflammatory syndromes, and type 2 diabetes. Furthermore, TR is active ex vivo for synovial fluid mononuclear cells from patients with gout. Thus, our study identifies the old drug TR as a direct NLRP3 inhibitor and provides a potentially practical pharmacological approach for treating NLRP3‐driven diseases.
Synopsis
Tranilast (TR), an anti‐allergic clinical drug, is here reported as a NLRP3 inflammasome inhibitor with beneficial effects for NLRP3‐driven diseases. By direct binding to NLRP3, it inhibits its oligomerization and subsequent inflammasome assembly, caspase‐1 activation and IL‐1β production.
TR specifically inhibits NLRP3 inflammasome activation in both human and mouse cells.
TR binds to NLRP3 and inhibits its oligomerization and inflammasome complex formation.
TR has remarkable preventive or therapeutic effects on the mouse models of NLRP3‐driven diseases.
Tranilast (TR), an anti‐allergic clinical drug, is here reported as a NLRP3 inflammasome inhibitor with beneficial effects for NLRP3‐driven diseases. By direct binding to NLRP3, it inhibits its oligomerization and subsequent inflammasome assembly, caspase‐1 activation and IL‐1β production.
Newborns are as the primary recipients of blood transfusions. There is a possibility of an association between blood transfusion and unfavorable outcomes. Such complications not only imperil the ...lives of newborns but also cause long hospitalization. Our objective is to explore the predictor variables that may lead to extended hospital stays in neonatal intensive care unit (NICU) patients who have undergone blood transfusions and develop a predictive nomogram. A retrospective review of 539 neonates who underwent blood transfusion was conducted using median and interquartile ranges to describe their length of stay (LOS). Neonates with LOS above the 75th percentile (P75) were categorized as having a long LOS. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to screen variables and construct a risk model for long LOS. A multiple logistic regression prediction model was then constructed using the selected variables from the LASSO regression model. The significance of the prediction model was evaluated by calculating the area under the ROC curve (AUC) and assessing the confidence interval around the AUC. The calibration curve is used to further validate the model's calibration and predictability. The model's clinical effectiveness was assessed through decision curve analysis. To evaluate the generalizability of the model, fivefold cross-validation was employed. Internal validation of the models was performed using bootstrap validation. Among the 539 infants who received blood transfusions, 398 infants (P75) had a length of stay (LOS) within the normal range of 34 days, according to the interquartile range. However, 141 infants (P75) experienced long LOS beyond the normal range. The predictive model included six variables: gestational age (GA) (< 28 weeks), birth weight (BW) (< 1000 g), type of respiratory support, umbilical venous catheter (UVC), sepsis, and resuscitation frequency. The area under the receiver operating characteristic (ROC) curve (AUC) for the training set was 0.851 (95% CI 0.805-0.891), and for the validation set, it was 0.859 (95% CI 0.789-0.920). Fivefold cross-validation indicates that the model has good generalization ability. The calibration curve demonstrated a strong correlation between the predicted risk and the observed actual risk, indicating good consistency. When the intervention threshold was set at 2%, the decision curve analysis indicated that the model had greater clinical utility. The results of our study have led to the development of a novel nomogram that can assist clinicians in predicting the probability of long hospitalization in blood transfused infants with reasonable accuracy. Our findings indicate that GA (< 28 weeks), BW(< 1000 g), type of respiratory support, UVC, sepsis, and resuscitation frequency are associated with a higher likelihood of extended hospital stays among newborns who have received blood transfusions.
The NLRP3 inflammasome can sense different pathogens or danger signals, and has been reported to be involved in the development of many human diseases. Potassium efflux and mitochondrial damage are ...both reported to mediate NLRP3 inflammasome activation, but the underlying, orchestrating signaling events are still unclear. Here we show that chloride intracellular channels (CLIC) act downstream of the potassium efflux-mitochondrial reactive oxygen species (ROS) axis to promote NLRP3 inflammasome activation. NLRP3 agonists induce potassium efflux, which causes mitochondrial damage and ROS production. Mitochondrial ROS then induces the translocation of CLICs to the plasma membrane for the induction of chloride efflux to promote NEK7-NLRP3 interaction, inflammasome assembly, caspase-1 activation, and IL-1β secretion. Thus, our results identify CLICs-dependent chloride efflux as an essential and proximal upstream event for NLRP3 activation.The NLRP3 inflammasome is key to the regulation of innate immunity against pathogens or stress, but the underlying signaling regulation is still unclear. Here the authors show that chloride intracellular channels (CLIC) interface between mitochondria stress and inflammasome activation to modulate inflammatory responses.
Machine learning plays an important role in building intrusion detection systems. However, with the increase of data capacity and data dimension, the ability of shallow machine learning is becoming ...more limited. In this paper, we propose a fuzzy aggregation approach using the modified density peak clustering algorithm (MDPCA) and deep belief networks (DBNs). To reduce the size of the training set and the imbalance of the samples, MDPCA is used to divide the training set into several subsets with similar sets of attributes. Each subset is used to train its own sub-DBNs classifier. These sub-DBN classifiers can learn and explore high-level abstract features, automatically reduce data dimensions, and perform classification well. According to the nearest neighbor criterion, the fuzzy membership weights of each test sample in each sub-DBNs classifier are calculated. The output of all sub-DBNs classifiers is aggregated based on fuzzy membership weights. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that our proposed model has higher overall accuracy, recall, precision and F1-score than other well-known classification methods. Furthermore, the proposed model achieves better performance in terms of accuracy, detection rate and false positive rate compared to the state-of-the-art intrusion detection methods.
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